ASR technology enables microphone-equipped computing devices to interpret speech and thereby provide an alternative to conventional human-to-computer input devices such as keyboards or keypads. For example, vehicle telecommunications devices are equipped with voice dialing features enabled by an ASR system. Typically, ASR users significantly vary the way they articulate their speech in response to changes in background noise of a vehicle. Vehicle background noise is influenced by several external and internal noises, such as the type of road the vehicle is traversing, the speed the vehicle travels, wind noise, noise external to the vehicle, air conditioning settings, and other factors. As vehicle background noise varies in intensity, ASR users tend to vary the volume and the pitch of their utterances. Such variation in user articulation in response to environmental influences is known as the Lombard effect and tends to impede an ASR system from quickly and accurately recognizing speech utterances.
Therefore, according to current ASR implementations, different acoustic models are empirically developed for different types of vehicle cabin environments, in an attempt to match actual conditions of in-vehicle speech recognition. But this approach can involve multitudes of different and unnecessarily complex acoustic models, thereby possibly delaying model development, increasing computing memory and power requirements, and yielding an unacceptable level of latency in speech recognition.